This paper proposes an approach to the problem of adaptation of neuralnetworks (NN) to arbitrary tasks. It is well known that the functionalproperties of a NN depend on its construction: on topological structure,learning and activation methods, and signal output. A definition language is developed for describing various constructions of NNs in the shape of strings. This paper uses a model of a neuron which has a receptive field and adaptable learning, activation and signaling, while the NN model consists of interconnected layers allowing feedforward, feedback and lateral connections with a single input and ouput layer. Adaptation of NNs is done with a genetic algorithm (GA)using crossover, mutation, and proportional selection operators on a population of strings that represent NNs. These strings (and their respective NNs) are evolved until they find solutions to given tasks which are defined as objective functions. The paper proposes a solution to »deception«,an important problem concerning GA's convergence: a strict hierarchy in the description of NNs based on ordered express ion which decreases the probability of dual representations. This approach can develop autodidactive NNs.


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    Title :

    Adaptation of Neural Networks Using Genetic Algorithms


    Contributors:

    Publication date :

    1995-02-01


    Remarks:

    Croatica Chemica Acta ; ISSN 0011-1643 (Print) ; ISSN 1334-417X (Online) ; Volume 68 ; Issue 1


    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    DDC:    629




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